A Multimodal AI Approach for Detecting Depression Severity Levels and Preventive Care

Major depressive disorder (MDD) is among the primary causes of disability in the world, and traditional diagnosis depends mostly on a baseless evaluation of a clinician. The recent developments in the field of artificial intelligence (AI) allow objective recognition of depressive symptoms on the basis of multimodal information like facial cues, vocal rhythms, and language. The study presents an integrative AI research proposal, which examines speech, text, and visual system synchronisation, estimating depression levels of severity and producing preventive-care suggestions that are specific to each risk profile. It utilizes big datasets of annotated texts such as Indian region speech corpora and visual interviews based on transformer-based encoders of an individual text to recognize emotions, convolutional featurebased text feature development, and acoustic embeddings concentrating on vocal stress to capture stress patterns of an individual. The suggested multimodal fusion model proves to be of high sensitivity and specificity on the classification of mild, moderate, or severe cases of depression, and a preventive-care module provides custom behavioural recommendations and tele-mental-health connexion. It aims to establish a base of operational clinical decision-support systems with AI-controlled monitoring incorporated into the operational systems of publichealth.